Our approach is limited to fairness considerations at the stage of inference. It does not address potential biases arising at other parts of the recommendation pipeline, such as in the estimation of preferences. Moreover, we considered a static model, which does not accounts for real-world dynamics, such as responsiveness in two-sided markets [1]}, feedback loops in the learning process [2]}, and the changing nature of the users' and items' populations [3]} and preferences [4]}. Addressing these limitations, in combination with our method, are interesting directions for future research.
